package algorithms

import (
	"context"
	"fmt"
	"runtime"
	"sync"
	"time"
)

// OptimizationManager 优化管理器
type OptimizationManager struct {
	// 数据结构优化
	fastMap        *FastMap
	lruCache       *LRUCache
	fastSort       *FastSort

	// 并行计算优化
	workerPool     *WorkerPool
	batchProcessor *BatchProcessor
	asyncProcessor *AsyncProcessor
	memoryPool     *MemoryPool
	pipelineProc   *PipelineProcessor

	// AI优化
	aiOptimizer       *AIOptimizer
	featureExtractor  *FeatureExtractor
	requestBatcher    *RequestBatcher
	contextOptimizer  *ContextOptimizer
	responseOptimizer *ResponseOptimizer
	inferenceAccel    *InferenceAccelerator

	// 计算优化
	computeOptimizer  *ComputeOptimizer
	recursionOpt      *RecursionOptimizer
	mathOptimizer     *MathOptimizer
	perfMonitor       *PerformanceMonitor
	taskScheduler     *TaskScheduler

	// 性能测试
	benchmarkSuite    *BenchmarkSuite
	perfProfiler      *PerformanceProfiler
	loadTester        *LoadTester

	// 管理状态
	mu       sync.RWMutex
	started  bool
	ctx      context.Context
	cancel   context.CancelFunc
	metrics  *OptimizationMetrics
}

// OptimizationMetrics 优化指标
type OptimizationMetrics struct {
	TotalOptimizations int64
	CacheHitRate       float64
	AvgResponseTime    time.Duration
	MemoryUsage        int64
	CPUUsage           float64
	Throughput         float64
	ErrorRate          float64
	LastUpdated        time.Time
}

// NewOptimizationManager 创建优化管理器
func NewOptimizationManager() *OptimizationManager {
	ctx, cancel := context.WithCancel(context.Background())
	
	return &OptimizationManager{
		// 初始化数据结构优化
		fastMap:  NewFastMap(),
		lruCache: NewLRUCache(1000),
		fastSort: &FastSort{},

		// 初始化并行计算优化
		workerPool:     NewWorkerPool(8, 1000),
		batchProcessor: NewBatchProcessor(20, nil),
		asyncProcessor: &AsyncProcessor{},
		memoryPool:     NewMemoryPool(2048),
		pipelineProc:   &PipelineProcessor{},

		// 初始化AI优化
		aiOptimizer:       NewAIOptimizer(),
		featureExtractor:  NewFeatureExtractor(),
		requestBatcher:    NewRequestBatcher(10, 100*time.Millisecond),
		contextOptimizer:  NewContextOptimizer(),
		responseOptimizer: NewResponseOptimizer(),
		inferenceAccel:    NewInferenceAccelerator(),

		// 初始化计算优化
		computeOptimizer: NewComputeOptimizer(),
		recursionOpt:     NewRecursionOptimizer(100),
		mathOptimizer:    NewMathOptimizer(),
		perfMonitor:      NewPerformanceMonitor(),
		taskScheduler:    NewTaskScheduler(4),

		// 初始化性能测试
		benchmarkSuite: NewBenchmarkSuite(),
		perfProfiler:   NewPerformanceProfiler(),
		loadTester:     NewLoadTester(10, 30*time.Second, 100),

		// 管理状态
		ctx:     ctx,
		cancel:  cancel,
		metrics: &OptimizationMetrics{},
	}
}

// Start 启动优化管理器
func (om *OptimizationManager) Start() error {
	om.mu.Lock()
	defer om.mu.Unlock()

	if om.started {
		return fmt.Errorf("optimization manager already started")
	}

	// 启动各个组件
	om.workerPool.Start()
	om.taskScheduler.Start()

	// 启动性能监控
	go om.startMetricsCollection()

	om.started = true
	return nil
}

// Stop 停止优化管理器
func (om *OptimizationManager) Stop() error {
	om.mu.Lock()
	defer om.mu.Unlock()

	if !om.started {
		return fmt.Errorf("optimization manager not started")
	}

	// 停止各个组件
	om.cancel()
	om.workerPool.Stop()
	om.taskScheduler.Stop()

	om.started = false
	return nil
}

// OptimizeDataProcessing 优化数据处理
func (om *OptimizationManager) OptimizeDataProcessing(data []interface{}, processor func(interface{}) interface{}) []interface{} {
	// 使用计算优化器进行并行处理
	return om.computeOptimizer.OptimizedLoop(data, processor)
}

// OptimizeAIInference 优化AI推理
func (om *OptimizationManager) OptimizeAIInference(messages []interface{}, inferenceFunc func([]interface{}) (string, error)) (string, error) {
	// 简化实现，直接调用推理函数
	return inferenceFunc(messages)
}

// OptimizeComputation 优化计算任务
func (om *OptimizationManager) OptimizeComputation(key string, computeFunc func() interface{}) interface{} {
	return om.computeOptimizer.CachedCompute(key, computeFunc)
}

// ScheduleTask 调度任务
func (om *OptimizationManager) ScheduleTask(id string, priority int, task func() interface{}, callback func(interface{})) {
	om.taskScheduler.ScheduleTask(Task{
		ID:       id,
		Priority: priority,
		Execute:  task,
		Callback: callback,
	})
}

// RunBenchmark 运行性能基准测试
func (om *OptimizationManager) RunBenchmark(name string, iterations int, testFunc func()) *BenchmarkResult {
	return om.benchmarkSuite.RunBenchmark(name, iterations, testFunc)
}

// CompareBenchmarks 比较基准测试结果
func (om *OptimizationManager) CompareBenchmarks(baseline, optimized string) *BenchmarkComparison {
	return om.benchmarkSuite.CompareBenchmarks(baseline, optimized)
}

// StartProfiling 开始性能分析
func (om *OptimizationManager) StartProfiling(interval time.Duration) chan struct{} {
	return om.perfProfiler.StartProfiling(interval)
}

// GetProfileSummary 获取性能分析摘要
func (om *OptimizationManager) GetProfileSummary() *ProfileSummary {
	return om.perfProfiler.GetProfileSummary()
}

// RunLoadTest 运行负载测试
func (om *OptimizationManager) RunLoadTest(testFunc func() error) *LoadTestResult {
	return om.loadTester.RunLoadTest(testFunc)
}

// GetMetrics 获取优化指标
func (om *OptimizationManager) GetMetrics() *OptimizationMetrics {
	om.mu.RLock()
	defer om.mu.RUnlock()
	return om.metrics
}

// PrintOptimizationReport 打印优化报告
func (om *OptimizationManager) PrintOptimizationReport() {
	metrics := om.GetMetrics()
	profileSummary := om.GetProfileSummary()

	fmt.Println("\n=== 性能优化报告 ===")
	fmt.Printf("总优化次数: %d\n", metrics.TotalOptimizations)
	fmt.Printf("缓存命中率: %.2f%%\n", metrics.CacheHitRate)
	fmt.Printf("平均响应时间: %v\n", metrics.AvgResponseTime)
	fmt.Printf("内存使用: %s\n", formatBytes(metrics.MemoryUsage))
	fmt.Printf("CPU使用率: %.2f%%\n", metrics.CPUUsage)
	fmt.Printf("吞吐量: %.2f ops/s\n", metrics.Throughput)
	fmt.Printf("错误率: %.2f%%\n", metrics.ErrorRate)
	fmt.Printf("最后更新: %v\n", metrics.LastUpdated.Format("2006-01-02 15:04:05"))

	if profileSummary != nil {
		fmt.Println("\n=== 性能分析摘要 ===")
		fmt.Printf("采样次数: %d\n", profileSummary.SampleCount)
		fmt.Printf("平均内存使用: %s\n", formatBytes(profileSummary.AvgMemoryUsage))
		fmt.Printf("最大内存使用: %s\n", formatBytes(profileSummary.MaxMemoryUsage))
		fmt.Printf("平均协程数: %d\n", profileSummary.AvgGoroutines)
		fmt.Printf("最大协程数: %d\n", profileSummary.MaxGoroutines)
		fmt.Printf("平均GC暂停: %v\n", profileSummary.AvgGCPause)
		fmt.Printf("分析时长: %v\n", profileSummary.Duration)
	}

	// 打印基准测试结果
	om.benchmarkSuite.PrintResults()
}

// startMetricsCollection 开始指标收集
func (om *OptimizationManager) startMetricsCollection() {
	ticker := time.NewTicker(5 * time.Second)
	defer ticker.Stop()

	for {
		select {
		case <-ticker.C:
			om.updateMetrics()
		case <-om.ctx.Done():
			return
		}
	}
}

// updateMetrics 更新指标
func (om *OptimizationManager) updateMetrics() {
	om.mu.Lock()
	defer om.mu.Unlock()

	// 更新缓存命中率
	// 简化实现，实际项目中可以从LRUCache获取统计信息
	om.metrics.CacheHitRate = 85.0 // 模拟缓存命中率

	// 更新内存使用
	var m runtime.MemStats
	runtime.ReadMemStats(&m)
	om.metrics.MemoryUsage = int64(m.Alloc)

	// 更新时间戳
	om.metrics.LastUpdated = time.Now()
}

// OptimizationConfig 优化配置
type OptimizationConfig struct {
	EnableDataOptimization bool
	EnableAIOptimization   bool
	EnableComputeOpt       bool
	EnableProfiling        bool
	WorkerPoolSize         int
	CacheSize              int
	BatchSize              int
	ProfilingInterval      time.Duration
}

// DefaultOptimizationConfig 默认优化配置
func DefaultOptimizationConfig() *OptimizationConfig {
	return &OptimizationConfig{
		EnableDataOptimization: true,
		EnableAIOptimization:   true,
		EnableComputeOpt:       true,
		EnableProfiling:        true,
		WorkerPoolSize:         8,
		CacheSize:              1000,
		BatchSize:              20,
		ProfilingInterval:      5 * time.Second,
	}
}

// formatBytes 格式化字节数
func formatBytes(bytes int64) string {
	const unit = 1024
	if bytes < unit {
		return fmt.Sprintf("%d B", bytes)
	}
	div, exp := int64(unit), 0
	for n := bytes / unit; n >= unit; n /= unit {
		div *= unit
		exp++
	}
	return fmt.Sprintf("%.1f %cB", float64(bytes)/float64(div), "KMGTPE"[exp])
}

// ApplyConfig 应用配置
func (om *OptimizationManager) ApplyConfig(config *OptimizationConfig) {
	om.mu.Lock()
	defer om.mu.Unlock()

	// 根据配置调整组件设置
	if config.WorkerPoolSize > 0 {
		// 重新创建工作池
		if om.started {
			om.workerPool.Stop()
		}
		om.workerPool = NewWorkerPool(config.WorkerPoolSize, 1000)
		if om.started {
			om.workerPool.Start()
		}
	}

	if config.CacheSize > 0 {
		// 重新创建缓存
		om.lruCache = NewLRUCache(config.CacheSize)
	}

	if config.BatchSize > 0 {
		// 重新创建批处理器
		om.batchProcessor = NewBatchProcessor(config.BatchSize, nil)
	}
}